A federal cubature Kalman filter for IMU-UWB indoor positioning

The tightly coupled IMU-UWB integration introduces high nonlinearity to the state and measurement equation of the Kalman filter so that the commonly used Extended Kalman Filtering method will produce a large truncation error, resulting in inaccurate fusion results. This paper proposes a new algorithm, called Federated Cubature Kalman Filtering (FCKF) method, by implementing the Cubature Kalman Filtering algorithm under the federated filtering framework. By implementing the proposed FCKF method, the observations of the UWB and the IMU are effectively fused, where the IMU is continuously calibrated by UWB so that it does not generate cumulative errors. In addition, it requires less computational burden than the classical Cubature Kalman Filtering method. Finally, the effectiveness of the proposed algorithm is verified by carrying out numerical simulations on two systems with different orders.